Method, Computer System and Non-Transitory Computer Readable Medium for Target Selection in the Vicinity of a Vehicle

ABSTRACT

Computer implemented method for target selection in the vicinity of a vehicle, comprising obtaining vehicle state information, the vehicle state information comprising dynamic information regarding the vehicle, predicting a first trajectory of the vehicle based on the vehicle state information for a first prediction time horizon, detecting road users in the vicinity of the vehicle, determining state information from the detected road users, the state information comprising dynamic information regarding the road users, predicting a second trajectory of the vehicle based on the vehicle state information and the road users state information for the first prediction time horizon and performing a first similarity comparison of the first predicted trajectory and the second predicted trajectory of the vehicle to determine whether the detected road users are a potential target of the vehicle for the first prediction time horizon.

INCORPORATION BY REFERENCE

This application claims priority to European Patent Application No.EP23151911.7, filed Jan. 17, 2023, and European Patent Application No.EP22153827.5, filed Jan. 28, 2022, the disclosures of which areincorporated by reference in their entireties.

FIELD

The present disclosure relates to a method, a computer system and anon-transitory computer readable medium for target selection in thevicinity of a vehicle, in particular in a vehicle with advanced driverassistance systems and/or an autonomously driving vehicle.

BACKGROUND

Driving of a vehicle involves constant interaction with other roadusers. Advanced driver assistance system (ADAS) features or autonomousdriving features of modern vehicles rely on the perception of these roadusers to adapt their own control parameters such as adaptive cruisecontrol (ACC), automatic emergency breaking (AEB)), to issue a warning(e.g. lateral collision warning (LCW), and/or for path planning for itsown autonomous driving (AD).

Target selection is traditionally a rule-based system and requiresdifferent rules for different applications. For example, an ACC systemmay adapt the speed of a vehicle according to be a target leadingvehicle, selected from a perceived road user. An autonomous drivingsystem may require to take into account various potential drivingbehaviors of road users and accordingly select appropriate targets toensure safe interaction with other users. However, selecting appropriatetargets is computationally intensive.

Accordingly, there is a need for an improved approach for targetselection of road users.

SUMMARY

The present disclosure provides a computer implemented method, acomputer system and a non-transitory computer readable medium, includingthose described in the independent claims. Embodiments are given in theclaims, the subclaims, the description and the drawings.

In one aspect, the present disclosure is directed at a computerimplemented method for target selection in the vicinity of a vehicle.The method comprises, in a first step, to obtain vehicle stateinformation. Therein, the vehicle state information comprises dynamicinformation regarding the vehicle. The method comprises, in a furtherstep, to predict a first trajectory of the vehicle based on the vehiclestate information for a first prediction time horizon. The methodcomprises, in another step, to detect, road users in the vicinity of thevehicle. The method comprises, in a further step, to determine stateinformation from the detected road users. Therein, the state informationcomprises dynamic information regarding the road users. The methodcomprises, in a further step, to predict a second trajectory of thevehicle based on the road users state information and on the vehiclestate information for the first prediction time horizon. The methodcomprises, in another step, to perform a first similarity comparison ofthe first predicted trajectory and the second predicted trajectory todetermine whether the detected road users are a potential target of thevehicle for the first prediction time horizon.

The vehicle may be a vehicle that has autonomous driving (AD) featuresor a vehicle with an advanced driving assistance system (ADAS). Thevehicle state information comprises dynamic information regarding thevehicle. Dynamic in this context means changing information and/orinformation related to a dynamic, i.e. moving context. In particular,the vehicle state information may comprise information regarding theposition, the steering angle, the throttle and/or brake input, theacceleration and/or the velocity of the vehicle, a turning signalstatus, AD and/or ADAS feature status and/or a current navigation route.This information may be obtained from suitable sensors, such as, forexample, odometry sensors being integrated into the vehicle. The vehiclestate information may in particular comprise vehicle state informationfrom the past.

Therein, in the method, the vehicle state information is processed topredict a first trajectory of the vehicle for a first prediction timehorizon. A prediction time horizon is a given point of time in thefuture for which the prediction is carried out. The first trajectorythat is predicted based on the vehicle state information is inparticular a future trajectory of the vehicle, i.e. a path that thevehicle may take in the future considering only the vehicle itself. Inparticular, the first trajectory is predicted only based on the vehiclestate information.

In a further step, road users in the vicinity of the vehicle aredetected. This may be performed by a sensor, such as, for example, oneor more image sensors, in particular one or more radar, lidar and/orcamera sensors being integrated into the vehicle.

The road users state information, similarly, comprises dynamicinformation regarding the road users. In particular, the stateinformation from the road users may comprise information regarding theposition, the acceleration and/or the velocity of other road users,different from the vehicle. A road user is, for example, anothervehicle, a motorbike or a truck using the same road and/or beingpositioned in the vicinity of the vehicle. The entirety of road usersmay comprise at least one other road user or multiple other road users.The road users state information may in particular comprise road usersstate information from the past, in particular from earlier and/orprevious prediction time horizons.

The road users state information is then processed together with thevehicle state information. Thereby, a second trajectory of the vehiclecan be predicted for the same first prediction time horizon. The secondtrajectory that is predicted based on the road users state informationand the vehicle state information is in particular a future trajectoryof the vehicle dependent on the other road users, i.e. a path that thevehicle may take in the future when considering the other road users asa whole and the vehicle itself.

Then, a first similarity comparison is carried out by comparing thefirst predicted trajectory of the vehicle, which is based on the vehiclestate information, and the second predicted trajectory of the vehicle,which is based on the road users state information and the vehicle stateinformation. Thereby, it is determined whether the road users are apotential target of the vehicle for the first prediction time horizon. Atarget is a road user that may have significant impact on the vehicle'sfuture driving trajectory. In particular, if the similarity comparisonyields a similarity below a predetermined threshold, the road user isconsidered a potential target. On the other hand, if the similaritycomparison yields a similarity greater than a predetermined threshold,the road user is not considered a potential target and may be discarded.

In other words, the first predicted trajectory, considering the vehicleonly, is compared with the second predicted trajectory, considering thevehicle and at least one other road user, and, if these two trajectoriesare similar to a certain extent, i.e. their similarity is a above acertain threshold, there is no significant difference between the twotrajectories, whereby the other road user is not considered a potentialtarget. If these two trajectories differ from each other to a certainextent, i.e. their similarity is below a certain threshold, there is asignificant difference between the two trajectories, whereby the otherroad user is considered a potential target.

According to an embodiment, the vehicle state information furthercomprises static information regarding the vehicle. Alternatively, oradditionally, the state information from road users comprises staticinformation regarding the road users.

Static in this context means unchanging information and/or informationrelated to a static, i.e. non-moving context. In particular, static inthis context means context that restricts the maneuver of the vehicleitself and/or of the particular road users or road user. As an example,the vehicle state information may comprise information regarding theposition of buildings, roads or other non-moving structures in thevicinity of the vehicle, such as the lane structure, a curb and/orguardrails. Additionally, or alternatively, the vehicle stateinformation may comprise information regarding traffic rules, speedlimitations, lane directions and/or traffic lights relevant for thevehicle.

Similarly, the state information from road users may compriseinformation regarding the position of buildings, roads or othernon-moving structures in the vicinity of the particular road users orroad user, such as the lane structure, a curb and/or guardrails.Additionally, or alternatively, the vehicle state information maycomprise information regarding traffic rules, speed limitations, lanedirections and/or traffic lights relevant for the particular road usersor road user.

The static information may be derived from map data and/or perceptionssensors, such as a camera-based traffic light detection, traffic signdetection and/or lane marking detection.

According to an embodiment, the step of performing the first similaritycomparison of the first predicted trajectory of the vehicle based on thevehicle state information and the second predicted trajectory of thevehicle based the road users state information and the vehicle stateinformation is used to determine a relevance threshold of the road usersfor the first prediction time horizon.

A relevance threshold is a threshold, above which other road users areconsidered a target and below which other road users are not considereda target for the vehicle. In particular, by considering all road users,an individual threshold can be determined for any given situation.

Thereby, road users that are not relevant for target determination arenot considered and computing power can be reduced.

According to an embodiment, the road users comprise a first road user.Therein, the step of determining state information from the detectedroad users comprises determining state information from the first roaduser. The method further comprises to predict a third trajectory of thevehicle based on the first road user state information and the vehiclestate information for the first prediction time horizon and to perform asecond similarity comparison of the second predicted trajectory of thevehicle, which is based on the road users state information and thevehicle state information, and the third predicted trajectory of thevehicle, which is based on the first road user state information and thevehicle state information, to determine whether the first road user is apotential target of the vehicle for the first prediction time horizonbased on the relevance threshold.

The state information from the first road user may comprise dynamicand/or static information as described above regarding an individualfirst road user out of all road users.

According to an embodiment, the road users comprise a second road user.Therein, the step of determining state information from the detectedroad users comprises determining state information from the second roaduser. The method further comprises to predict a fourth trajectory of thevehicle based on the second road user state information and the vehiclestate information for the first prediction time horizon and to perform athird similarity comparison of the fourth predicted trajectory of thevehicle, which is based on the second road user state information andthe vehicle state information, and the second predicted trajectory ofthe vehicle, which is based on the road users state information and thevehicle state information, to determine whether the second road user isa potential target of the vehicle for the first prediction time horizonbased on the relevance threshold.

Likewise, the state information from the second road user may comprisedynamic and/or static information as described above regarding anindividual second road user out of all road users.

Therein, the first road user is different from the second road user andboth are different from the vehicle.

Thereby, the road users may be ranked based on their impact on thevehicle.

According to an embodiment, the steps of the method are repeated for asecond prediction time horizon different from the first prediction timehorizon.

In particular, the steps may be repeated for a second prediction timehorizon after concluding all the steps for the first prediction timehorizon, i.e. sequentially. In another embodiment, the steps areprocessed in parallel for a first and a second prediction time horizon.

The second prediction time horizon is another point in time in thefuture for which the prediction is carried out, in particular, a pointin time further in the future as the first prediction time horizon.

Thereby, a particular robust target determination can be achieved.

According to an embodiment, the similarity comparison comprises toperform a distance metric.

According to an embodiment, the distance metric comprises performing atleast one of a Wasserstein, an L1, an L2 or a Mahalanobis algorithm.

According to an embodiment, the predictions are performed by amachine-learning algorithm. In particular, the prediction of the firsttrajectory, the second trajectory, the third trajectory and/or thefourth trajectory may be performed by using a machine-learningalgorithm.

The machine-learning algorithm may be in particular a context-awarescene prediction algorithm. In particular, the machine-learningalgorithm may be a multi-modal prediction algorithm based onmachine-learning. Further, the machine-learning algorithm may use aconvolutional neural network and/or a recurrent neural network, whichmay be used in particular together to jointly learn and predict themotion of one or more road users.

In particular, for the prediction of a given scene of road users, thefuture motion of one or more road users in a region of interest ispredicted. The number of road users may vary from scene to scene whichleads to the requirement of storing a variable number of road users in ashared data structure. As input and output data structure, a series offixed-size 2D grids, which may also be referred to as images, is used,which allows the algorithm to jointly encode the trajectories of one ormore road users in a region of interest and support the prediction ofthe road users.

The predictions may be performed as described in Maximilian Schaefer etal.: “Context-Aware Scene Prediction Network (CASPNet)”,https://arxiv.org/abs/2201.06933, the entire contents of which isincorporated herein by reference.

Likewise, the prediction may be performed as described in M. Schäfer, K.Zhao, M. Bühren and A. Kummert, “Context-Aware Scene Prediction Network(CASPNet),” 2022 IEEE 25th International Conference on IntelligentTransportation Systems (ITSC), Macau, China, 2022, pp. 3970-3977, doi:10.1109/ITSC55140.2022.9921850, the entire contents of which isincorporated herein by reference.

In another aspect, the present disclosure is directed at a computersystem, said computer system being configured to carry out several orall steps of the computer implemented method described herein.

The computer system may comprise a processing unit, at least one memoryunit and at least one non-transitory data storage. The non-transitorydata storage and/or the memory unit may comprise a computer program forinstructing the computer to perform several or all steps or aspects ofthe computer implemented method described herein.

In another aspect, the present disclosure is directed at a vehicle,comprising a computer system as described above.

In another aspect, the present disclosure is directed at anon-transitory computer readable medium comprising instructions forcarrying out several or all steps or aspects of the computer implementedmethod described herein. The computer readable medium may be configuredas: an optical medium, such as a compact disc (CD) or a digitalversatile disk (DVD); a magnetic medium, such as a hard disk drive(HDD); a solid state drive (SSD); a read only memory (ROM), such as aflash memory; or the like. Furthermore, the computer readable medium maybe configured as a data storage that is accessible via a dataconnection, such as an internet connection. The computer readable mediummay, for example, be an online data repository or a cloud storage.

The present disclosure is also directed at a computer program forinstructing a computer to perform several or all steps or aspects of thecomputer implemented method described herein.

DRAWINGS

Example embodiments and functions of the present disclosure aredescribed herein in conjunction with the following drawings, showingschematically:

FIG. 1 illustrates a system for target selection in the vicinity of avehicle,

FIG. 2 illustrates a flow chart of a method for target selection in thevicinity of a vehicle, and

FIGS. 3A, 3B, 3C, 3D, 3E, and 3F illustrate an example outcome of themethod for target selection in the vicinity of a vehicle.

DETAILED DESCRIPTION

FIG. 1 depicts a system 100 for target selection in the vicinity of avehicle. The system 100 comprises a processor 10, a memory 20, anodometry sensor 30 and an image sensor 40.

The system 100 is adapted to obtain, by means of the processor 10,vehicle state information. Therein, the vehicle state informationcomprises dynamic and static information regarding the vehicle. Thedynamic vehicle state information are obtained by means of the odometrysensor 30 and the static vehicle state information are obtained by meansof the processor from data stored in the memory 20.

The system 100 is further adapted to perform, by means of the processor10, a machine-learning algorithm to predict a first trajectory of thevehicle based on the vehicle state information for a first predictiontime horizon.

The system 100 is further adapted to detect, by means of the imagesensor 40, road users in the vicinity of the vehicle. The system 100 isfurther adapted to determine, by means of the processor 10, stateinformation from the detected road users, which are different from thevehicle. Therein, the state information comprises dynamic and staticinformation regarding the road users.

The system 100 is further adapted to perform, by means of the processor10, a machine-learning algorithm on the road users state information andthe vehicle state information to predict a second trajectory of thevehicle for the first prediction time horizon.

The system 100 is further adapted to perform, by means of the processor10, a first similarity comparison of the first predicted trajectory ofthe vehicle, based on the vehicle state information, and the secondpredicted trajectory of the vehicle, based on the road users stateinformation and the vehicle state information, to determine whether theroad users are a potential target of the vehicle for the firstprediction time horizon.

Therein, the step of performing, by the processor 10, the firstsimilarity comparison of the first predicted trajectory of the vehiclebased on the vehicle state information and the second predictedtrajectory of the vehicle based the road users state information and thevehicle state information is used to determine a relevance threshold ofthe detected road users for the first prediction time horizon.

Therein, the road users comprise a first road user and the step ofdetermining state information from road users comprises determiningstate information from the first road user. Therein, the system 100 isfurther adapted to perform, by means of the processor 10, amachine-learning algorithm on the first road user state information andthe vehicle state information to predict a third trajectory of thevehicle for the first prediction time horizon and to perform, by meansof the processor 10, a second similarity comparison of the predictedtrajectory of the vehicle based on the second predicted trajectory ofthe vehicle based on the road users state information and the vehiclestate information and the third predicted trajectory of the vehiclebased on the first road user state information and the vehicle stateinformation to determine whether the first road user is a potentialtarget of the vehicle for the first prediction time horizon based on therelevance threshold.

Further, the road users comprise a second road user, wherein the step ofdetermining state information from road users comprises determiningstate information from the second road user. Therein, the system 100 isfurther adapted to perform, by means of the processor 10, amachine-learning algorithm on the second road user state information andthe vehicle state information to predict a fourth trajectory of thevehicle for the first prediction time horizon, and to perform, by meansof the processor 10, a third similarity comparison of the fourthpredicted trajectory of the vehicle based on the second road user stateinformation and the vehicle state information and the second predictedtrajectory of the vehicle based on the road users state information andthe vehicle state information to determine whether the second road useris a potential target of the vehicle for the first prediction timehorizon based on the relevance threshold.

The system 100 is further adapted to determine whether the first roaduser or the second road user has a higher priority based on the secondsimilarity comparison and the third similarity comparison.

The system 100 is further adapted to repeat, by means of the processor10, the previously described steps for a second prediction time horizondifferent from the first prediction time horizon.

The system 100 is further adapted to perform a distance metric, whichmay comprise at least one of a Wasserstein, an L1, an L2 or aMahalanobis algorithm.

In particular, the system 100 will now be described exemplarily withrespect to individual features that may be underlying the system 100:

A human driver's driving trajectory is affected by its own dynamic andits surrounding context, which consists of other road users (dynamiccontext) and the static environment or context. Given the vehicle's pastdynamic state x, all N other road users' past dynamic state x₁, x₂, . .. , x_(n), the static information of the scene as c, the futuretrajectory of the vehicle y_(t) at future prediction time horizon t canbe defined as the conditional distribution:

P(y _(t) |x,x ₁ ,x ₂ , . . . ,x _(n) ,c)  (1)

In particular, the intention, as a thought of the human, of where thedriver wants to go cannot be observed from sensors. For example, infront of an intersection, the driver may go left, or right, or straight,its trajectory may be affected by its context, but the most importantfactor is where does the driver want to go.

This factor is non-observable. Thus, the predicted distribution fory_(t) has to be multi-modal, to cover the multiple possible true futuretrajectories. Depending on where the driver may want to go, the targetsmay also be different.

Therein, the vehicle's future trajectory based on a single road userx_(i)'s impact can be formulated as a conditional distribution P_(i)

P _(i)(t _(t) |x,x _(i) ,c),  (2)

Similar, the distribution P₀ describes the trajectory distribution whenno dynamic context (no other road users) are taken into considerationfor the prediction of y_(t)

P ₀(t _(t) |x,c)  (3)

Assume there is a distance function L, which measures the distance dbetween two distributions. For example, between distributions (1) and(2)

d _(i) =L(P,P _(i))  (4)

Also, with and without dynamic context the predicted distribution'sdistance can be calculated as

d ₀ =L(P,P ₀)  (5)

Fundamentally, a small distance means similar predictions distribution,and great distance indicates different distributions.

Given the above equations and definitions, the system 100 for targetselection may be described as the following procedure:

-   1. At each prediction time horizon,    -   a. Predict the distribution P, P_(i) i∈[1, 2, . . . , N] and P₀        using the machine-learning algorithm.    -   b. Check whether the dynamic context plays a role for prediction        at all by calculating the distance d₀=L(P, P₀(t_(t)|x, c)).    -   c. Loop through all the road users, for each road user x_(i)        calculate its distance to distribution P:

d _(i) =L(P,P _(i))

-   -   d. Keep the road users, whose distance to P is smaller than d₀.    -   e. The road user with the smallest distance means it has most        significant impact on vehicle's driving.

-   2. Repeat above step for all the prediction time horizons, to select    the most relevant targets (if there are any) for each prediction    time horizon.

Thereby, a machine-learning-based, context-aware trajectory predictionsystem is used to test which road user has the most significant impacton the vehicle's future trajectory, by comparing the predictions betweenfully context-aware and with only one road user as dynamic context. Inaddition, whether the road users (dynamic context) plays has an impactat all is also tested.

This will now be described in further detail with respect to FIG. 2 ,which shows a flow chart of a method 200 for target selection in thevicinity of a vehicle.

In step 210, the method 200 obtains vehicle state information.

In step 220 then a machine-learning algorithm is performed to predict afirst trajectory of the vehicle based on the vehicle state informationfor a first prediction time horizon.

The prediction is put out in step 230.

In step 240, state information from road users different from thevehicle are obtained.

In step 250 a machine-learning algorithm is performed on the road usersstate information and the vehicle state information to predict a secondtrajectory of vehicle for the first prediction time horizon.

The prediction is put out in step 260.

Then, in step 265, a first similarity comparison is performed based onthe first predicted trajectory of the vehicle as put out in step 230 andthe second predicted trajectory of the vehicle as put out in step 260 todetermine whether the road users are a potential target of the vehiclefor the first prediction time horizon.

In step 270, state information from only one road user, i.e. a firstroad user, is used as input together with the vehicle state information,on which the machine-learning algorithm is performed in step 280 topredict a third trajectory of the vehicle for the first prediction timehorizon, which is put out at 290.

Then, in step 295, a second similarity comparison is performed based onthe second predicted trajectory of the vehicle as put out in step 260and the third predicted trajectory of the vehicle as put out in step 290to determine whether the one road user is a potential target of thevehicle for the first prediction time horizon.

This is done based on a relevance threshold previously determined basedon first similarity comparison of step 265.

These last four steps 270, 280, 290 and 295 may then be repeated basedon state information of another one of the road users, i.e. a second, athird and/or a fourth road user together with the vehicle stateinformation to perform a third and/or a fourth similarity comparison.

Similarly, the method 200 may be repeated for a second prediction timehorizon, a third time horizon, a fourth time horizon, etc., eithersequentially or in parallel.

Through the above-described system 100 and method 200 it is possible toprovide a general target selection framework, which works regardless ofhighway/urban, road structures and driving scenarios as well asregardless of the complexity of the scene.

In particular, through the above-described embodiments, a data-drivenapproach is provided that learned from real world user driving, whereinno specific rules need to be explicitly defined, which mimics arealistic decision of the user.

The target determination convers the various possible driving behaviorsof the user, which considers multi-modality. Thereby, the targetdetermination is independent of the application and further providesflexibility for different target determinations depending on differentprediction time horizons.

FIGS. 3A, 3B, 3C, 3D, 3E, and 3F show an exemplary outcome of the methodfor target selection in the vicinity of a vehicle 1000 as described inconjunction with FIG. 2 .

Therein, a multi-lane intersection 1001 is shown for a first predictionhorizon 1100 at 0.5 s in the future, a second prediction horizon 1200 at1.0 s in the future, a third prediction horizon 1300 at 1.5 s in thefuture, a fourth prediction horizon 1400 at 2.0 s in the future, a fifthprediction horizon 1500 at 2.5 s in the future and a sixth predictionhorizon 1600 at 3.0 s in the future.

Therein, the vehicle 1000, on which the method for target selection isperformed, is shown in the center of the individual prediction horizon.In the first prediction horizon 1100 none of the other road users are apotential target of the vehicle. This is due to the short future that ispredicted in the first prediction horizon 1100.

In the second prediction horizon 1200, out of the multiple road users,four road users have been identified as potential targets of the vehicle1000, i.e. a first road user 1010, a second road user 1020 and a thirdroad user 1030. The other road users, which are not shown with areference numeral, have been identified as not relevant or below apreviously determined relevance threshold.

Therein, a smaller number for the respective road user indicates ahigher relevance. As can be seen in the second prediction horizon 1200,the method has identified the third road user 1030 as most relevant, thesecond road user 1020 as less relevant and the first road user 1010 asleast relevant.

However, as can be seen from the third prediction horizon 1300, thefirst road user 1010 has not been identified as relevant any longer,wherein the second road user 1020 is now considered the most relevantroad user, followed by the third road user 1030. This remains the samein the fourth prediction horizon 1040.

As can be seen from the fifth prediction horizon 1050, now the secondroad user 1020 is considered the only relevant road user. In the sixthprediction horizon 1060, however, the third road user becomes relevantagain, however, with a much lower relevance.

Unless context dictates otherwise, use herein of the word “or” may beconsidered use of an “inclusive or,” or a term that permits inclusion orapplication of one or more items that are linked by the word “or” (e.g.,a phrase “A or B” may be interpreted as permitting just “A,” aspermitting just “B,” or as permitting both “A” and “B”). Also, as usedherein, a phrase referring to “at least one of” a list of items refersto any combination of those items, including single members. Forinstance, “at least one of a, b, or c” can cover a, b, c, a-b, a-c, b-c,and a-b-c, as well as any combination with multiples of the same element(e.g., a-a, a-a-a, a-a-b, a-a-c, a-b-b, a-c-c, b-b, b-b-b, b-b-c, c-c,and c-c-c, or any other ordering of a, b, and c). Further, itemsrepresented in the accompanying figures and terms discussed herein maybe indicative of one or more items or terms, and thus reference may bemade interchangeably to single or plural forms of the items and terms inthis written description.

REFERENCE NUMERAL LIST

-   10 processor-   20 memory-   30 odometry sensor-   40 image sensor-   100 system-   200 method-   210 method step-   220 method step-   230 method step-   240 method step-   250 method step-   260 method step-   265 method step-   270 method step-   280 method step-   290 method step-   295 method step-   1000 vehicle-   1001 intersection-   1010 first road user-   1020 second road user-   1030 third road user-   1040 fourth road user-   1100 first prediction horizon-   1200 second prediction horizon-   1300 third prediction horizon-   1400 fourth prediction horizon-   1500 fifth prediction horizon-   1600 sixth prediction horizon

1. A method comprising: obtaining, by a processor, vehicle stateinformation, the vehicle state information comprising dynamicinformation regarding a vehicle; predicting, by the processor, a firsttrajectory of the vehicle based on the vehicle state information for afirst prediction time horizon; detecting, by a sensor of the vehicle,road users in a vicinity of the vehicle; determining state informationfrom the detected road users, the state information comprising dynamicinformation regarding the road users; predicting, by the processor, asecond trajectory of the vehicle based on the vehicle state informationand the road users state information for the first prediction timehorizon; and performing, by the processor, a first similarity comparisonof the first predicted trajectory and the second predicted trajectory ofthe vehicle to determine whether the detected road users are a potentialtarget of the vehicle for the first prediction time horizon.
 2. Themethod according to claim 1, wherein the dynamic information comprisesinformation regarding at least one of: a position of the vehicle; asteering angle of the vehicle; a throttle input of the vehicle; a brakeinput of the vehicle; an acceleration of the vehicle; a velocity of thevehicle; a turning signal status of the vehicle; an autonomous drivingfeature status; or a navigation route of the vehicle.
 3. The methodaccording to claim 1, wherein the vehicle state information furthercomprises static information regarding the vehicle.
 4. The methodaccording to claim 1, wherein the step of performing, by the processor,the first similarity comparison is used to determine a relevancethreshold of the detected road users for the first prediction timehorizon.
 5. The method according to claim 4, wherein the stateinformation from road users comprises state information from a firstroad user, and wherein the method further comprises: predicting, by theprocessor, a third trajectory of the vehicle based on the first roaduser state information and the vehicle state information for the firstprediction time horizon; and performing, by the processor, a secondsimilarity comparison of the second predicted trajectory and the thirdpredicted trajectory to determine whether the first road user is apotential target of the vehicle for the first prediction time horizonbased on the relevance threshold.
 6. The method according to claim 5,wherein the state information from road users comprises stateinformation from a second road user, and wherein the method furthercomprises: predicting, by the processor, a fourth trajectory of thevehicle based on the second road user state information and the vehiclestate information for the first prediction time horizon; and performing,by the processor, a third similarity comparison of the second predictedtrajectory and the fourth predicted trajectory to determine whether thesecond road user is a potential target of the vehicle for the firstprediction time horizon based on the relevance threshold.
 7. The methodaccording to claim 6, further comprising: determining whether the firstroad user or the second road user has a higher priority based on thesecond similarity comparison and the third similarity comparison.
 8. Themethod according to claim 1, wherein the steps of the method arerepeated for a second prediction time horizon different from the firstprediction time horizon.
 9. The method according to claim 1, wherein thefirst similarity comparison comprises performing a distance metric. 10.The method according to claim 9, wherein the distance metric comprisesat least one of: performing a Wasserstein algorithm; performing an L1algorithm; performing an L2 algorithm; or performing an Mahalanobisalgorithm.
 11. The method according to claim 1, wherein the predictionis performed by using a machine-learning algorithm.
 12. A computersystem comprising: a processor; and a sensor, the computer systemconfigured to: obtain, by the processor, vehicle state information, thevehicle state information comprising dynamic information regarding avehicle; predict, by the processor, a first trajectory of the vehiclebased on the vehicle state information for a first prediction timehorizon; detect, by the sensor, road users in a vicinity of the vehicle;determine state information from the detected road users, the stateinformation comprising dynamic information regarding the road users;predict, by the processor, a second trajectory of the vehicle based onthe vehicle state information and the road users state information forthe first prediction time horizon; and perform, by the processor, afirst similarity comparison of the first predicted trajectory and thesecond predicted trajectory of the vehicle to determine whether thedetected road users are a potential target of the vehicle for the firstprediction time horizon.
 13. A non-transitory computer readable mediumcomprising instructions that when executed by a computer cause thecomputer to perform a method for target selection in a vicinity of avehicle, the method comprising: obtaining, by a processor, vehicle stateinformation, the vehicle state information comprising dynamicinformation regarding the vehicle; predicting, by the processor, a firsttrajectory of the vehicle based on the vehicle state information for afirst prediction time horizon; detecting, by a sensor of the vehicle,road users in a vicinity of the vehicle; determining state informationfrom the detected road users, the state information comprising dynamicinformation regarding the road users; predicting, by the processor, asecond trajectory of the vehicle based on the vehicle state informationand the road users state information for the first prediction timehorizon; and performing, by the processor, a first similarity comparisonof the first predicted trajectory and the second predicted trajectory ofthe vehicle to determine whether the detected road users are a potentialtarget of the vehicle for the first prediction time horizon.
 14. Thecomputer system of claim 12, further comprising: a vehicle.